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<div class="brand-logo">ML</div>
<span>ML Academy</span>
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<span>Formation 2025/2026 — 100% Pratique</span>
</div>
<h1 class="hero-title">
Maitrisez le <span class="gradient-text">Machine Learning</span><br>
par la pratique
</h1>
<p class="hero-subtitle">
Apprenez les algorithmes ML essentiels a travers des projets concrets
avec des datasets reels de Kaggle. De zero a heros en quelques seances.
</p>
<div class="hero-cta">
<a href="cours.html" class="btn btn-primary btn-lg">
Commencer les cours
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Voir les TPs
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<div class="hero-stat">
<div class="hero-stat-value">8<span>+</span></div>
<div class="hero-stat-label">Algorithmes</div>
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<div class="hero-stat">
<div class="hero-stat-value">6<span></span></div>
<div class="hero-stat-label">TPs Complets</div>
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<div class="hero-stat">
<div class="hero-stat-value">4<span></span></div>
<div class="hero-stat-label">Datasets Reels</div>
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<div class="hero-stat">
<div class="hero-stat-value">0<span></span></div>
<div class="hero-stat-label">Installation</div>
</div>
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</section>
<!-- Features Section -->
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<div class="section-eyebrow">Pourquoi cette formation</div>
<h2 class="section-title">Ce que vous allez apprendre</h2>
<p class="section-subtitle">
Une approche progressive alliant theorie solide et pratique intensive
sur des problemes reels du monde de la data science.
</p>
</div>
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<div class="feature-card scroll-animate">
<div class="feature-icon">D</div>
<h3 class="feature-title">Pretraitement des Donnees</h3>
<p class="feature-text">
Maitrisez le nettoyage, la normalisation et l'ingenierie des features
pour preparer vos donnees a l'entrainement.
</p>
<div class="feature-tags">
<span class="badge badge-primary">Pandas</span>
<span class="badge badge-secondary">NumPy</span>
<span class="badge badge-success">Scikit-learn</span>
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</div>
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<div class="feature-icon">A</div>
<h3 class="feature-title">Algorithmes de ML</h3>
<p class="feature-text">
Comprendre et implementer les algorithmes fondamentaux :
Regression, Classification, Clustering et Deep Learning.
</p>
<div class="feature-tags">
<span class="badge badge-primary">Regression</span>
<span class="badge badge-secondary">Random Forest</span>
<span class="badge badge-accent">Neural Networks</span>
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<div class="feature-icon">E</div>
<h3 class="feature-title">Evaluation & Optimisation</h3>
<p class="feature-text">
Apprenez a mesurer la performance de vos modeles et a les optimiser
avec les bonnes metriques et techniques.
</p>
<div class="feature-tags">
<span class="badge badge-success">Cross-validation</span>
<span class="badge badge-warning">Grid Search</span>
<span class="badge badge-primary">Metriques</span>
</div>
</div>
<div class="feature-card scroll-animate">
<div class="feature-icon">P</div>
<h3 class="feature-title">Projets Concrets</h3>
<p class="feature-text">
Travaillez sur des datasets reels de Kaggle : Titanic, Housing,
Iris et bien d'autres pour construire votre portfolio.
</p>
<div class="feature-tags">
<span class="badge badge-success">Kaggle</span>
<span class="badge badge-accent">Portfolio</span>
<span class="badge badge-secondary">GitHub</span>
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</div>
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<div class="feature-icon">T</div>
<h3 class="feature-title">Series Temporelles</h3>
<p class="feature-text">
Decouvrez les techniques specifiques pour predire des donnees
temporelles avec LSTM et les modeles ARIMA.
</p>
<div class="feature-tags">
<span class="badge badge-primary">LSTM</span>
<span class="badge badge-secondary">TensorFlow</span>
<span class="badge badge-warning">Time Series</span>
</div>
</div>
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<div class="feature-icon">X</div>
<h3 class="feature-title">Deploiement</h3>
<p class="feature-text">
Apprenez a mettre vos modeles en production avec des API
et des interfaces web interactives.
</p>
<div class="feature-tags">
<span class="badge badge-success">FastAPI</span>
<span class="badge badge-primary">Streamlit</span>
<span class="badge badge-secondary">Docker</span>
</div>
</div>
</div>
</section>
<!-- Modules Section -->
<section class="modules">
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<div class="section-eyebrow">Parcours de formation</div>
<h2 class="section-title">Accedez aux modules</h2>
<p class="section-subtitle">
Trois espaces dedies pour une progression optimale dans votre apprentissage.
</p>
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<a href="cours.html" class="module-card scroll-animate">
<div class="module-header">
<div class="module-icon blue">C</div>
<div class="module-info">
<div class="module-label">Module 1</div>
<h3 class="module-title">Cours Theoriques</h3>
</div>
</div>
<div class="module-body">
<p class="module-desc">
Contenu theorique complet avec equations mathematiques,
explications detaillees et exemples de code Python annote.
</p>
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